class_docstr: Anomalib dataset.
  
  Args:
      task (str): Task type, either 'classification' or 'segmentation'
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
docstr: Load data/metadata into memory.
docstr_tok: ["Load","data\/metadata","into","memory","."]
code: def setup(self) -> None:
      """Load data/metadata into memory."""
      if not self.is_setup:
          self._setup()
      assert self.is_setup, "setup() should set self._samples"
  
imports: from __future__ import annotations
  import copy
  import logging
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Sequence
  import albumentations
  import cv2
  import numpy
  import pandas
  from pandas import DataFrame
  from torch import Tensor
  from torch.utils.data import Dataset
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import masks_to_boxes, read_image
label_desc: Use image node_id 11 for calling the AnomalibDataset obj's underlying member method code with example usage: obj.setup() without return types